Linking Core Promoter Classes to Circadian
Transcription
Pål O. Westermark*
Institute for Theoretical Biology, Charité–Universitätsmedizin Berlin, Berlin, Germany
*pal-olof.westermark@charite.de
Abstract
Circadian rhythms in transcription are generated by rhythmic abundances and DNA binding activities of transcription factors. Propagation of rhythms to transcriptional initiation involves the core promoter, its chromatin state, and the basal transcription machinery. Here, I char-acterize core promoters and chromatin states of genes transcribed in a circadian manner in mouse liver and inDrosophila. It is shown that the core promoter is a critical determinant of
circadian mRNA expression in both species. A distinct core promoter class, strong circadian promoters (SCPs), is identified in mouse liver but notDrosophila. SCPs are defined by
spe-cific core promoter features, and are shown to drive circadian transcriptional activities with both high averages and high amplitudes. Data analysis and mathematical modeling further provided evidence for rhythmic regulation of both polymerase II recruitment and pause release at SCPs. The analysis provides a comprehensive and systematic view of core pro-moters and their link to circadian mRNA expression in mouse andDrosophila, and thus
reveals a crucial role for the core promoter in regulated, dynamic transcription.
Author Summary
Circadian rhythms switch gene expression on and off with a daily rhythm in most tissues in mammals and other animals. Typically, thousands of genes are affected, and the func-tions of these rhythms include preparing and adjusting various physiological funcfunc-tions in tissues to meet time-of-day dependent requirements optimally. The controllers of the rhythms are often transcription factors (proteins which regulate transcription), which are relatively well known. However, there is a layer between transcription factor action and transcriptional activity whose role in circadian transcription has not previously been char-acterized: the core promoter. The core promoter acts as a template for the assembly of the intricate machinery that governs initiation of transcription. There are different types of core promoters that are typically used for different types of genes. It is not known which types of core promoters are used for the rhythmically induced genes. Here, it is shown that there are specific characteristics of core promoters driving rhythmic transcription in mice and flies. Furthermore, it is shown that there is a class of strong circadian promoters in mice that drive strong rhythms with very intense average transcriptional rates. These results help understanding the regulatory systems governing circadian transcription, and
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OPEN ACCESS
Citation:Westermark PO (2016) Linking Core Promoter Classes to Circadian Transcription. PLoS Genet 12(8): e1006231. doi:10.1371/journal. pgen.1006231
Editor:David Gatfield, Center for Integrative Genomics, SWITZERLAND
Received:January 27, 2016
Accepted:July 8, 2016
Published:August 9, 2016
Copyright:© 2016 Pål O. Westermark. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability Statement:Accession numbers for all source data are included in the manuscript, and summarized here. In the following, all accession numbers beginning with“GSE”or“GSM”refer to the NCBI Gene Expression Omnibus (GEO) archive. Mouse liver Nascent-Seq and RNA-Seq data: NCBI sequence read archive, SRP011984 and SRP01198, respectively. Microarray data, mouse liver: GSE33726. Drosophila head Nascent-Seq and RNA-Seq data: NCBI sequence read archive, SRP012175. ChIP-Seq peaks for BMAL1: doi:10.1371/journal.pbio. 1000595.s019. ChIP-Seq peaks for REV-ERBαand
ultimately, aid the understanding of many diseases resulting from disturbed circadian rhythms.
Introduction
In many metazoans, transcription of numerous genes in most cell types occurs in a rhythmic fashion with a period of ~24 hours, also if the organism is held under constant conditions; these rhythms are termed circadian transcriptional rhythms [1]. They are to a large extent orchestrated by the cellular circadian clock, which consists of connected feedback loops of clock genes that code for clock proteins. In turn, clock proteins repress or activate transcription of themselves or other clock genes, in this way generating the rhythms [2]. When organisms are held under rhyth-mic conditions, such as 12hr/12hr light-dark cycles, the circadian clocks in their cells become synchronized to these external so-called zeitgebers. Strictly, one then speaks of diurnal rhythms in gene expression, although here we use the term circadian rhythms also for this case.
The cellular circadian clock becomes manifest in an output of rhythmic mRNA expression of typically hundreds or thousands of clock-controlled genes (CCGs) [3]. The CCGs are thought to be controlled to a large extent by transcription factors (TFs) or coregulators with rhythmic abun-dances that are part of the cellular circadian clock. In mammals, prominent examples are the CLOCK/BMAL1 heterodimer, which binds to E-boxes in promoters of its target genes [4], the nuclear receptors REV-ERBαandβ, which bind to ROR elements [5], and DBP and E4BP4, which bind to D-boxes [6,7]. In the flyDrosophila melanogaster, the CLOCK homolog CLK has a similar function, also binding to E-boxes [8]. Rhythmic binding of these core circadian clock TFs (CTFs) and their coregulators to promoters are thought to induce rhythms in transcriptional activities, which ultimately lead to rhythms in mRNA and protein abundances.
Circadian transcriptional rhythms have been investigated by combined analysis of tran-script abundances and CTF binding to corresponding promoters. Mouse liver andDrosophila
have served as useful model systems for investigating transcriptional rhythms, and by for instance comparing nascent transcript to mature mRNA abundances, the propagation of rhythms from transcriptional activities to transcript abundances can be monitored [9–12]. Anal-ysis of such data has revealed a contribution of post-transcriptional regulation to the generation of rhythms in mature mRNA abundances, but transcriptional rhythms remain the dominant determinant of the rhythmic transcriptome [13,14]. Circadian rhythm generation is correlated with rhythmic binding of CTFs to binding sites in the promoters of genes with circadian mRNA expression: for instance, BMAL1 binding phases (peak time), measured at the promoter level in mouse liver, correlate well with phases of the corresponding transcripts [4]. On the bioinformat-ics and data analysis side, position weight matrix (PWM) based prediction of TF binding sites at the promoter population level paired with quantitative modeling has led to additional insights into the combinatorial regulation of rhythmic transcription by CTFs and also other circadian TFs [15–17]. However, the layer of mechanistic rhythm propagation between CTF binding and transcriptional rhythms has not been characterized systematically. This layer consists of the core promoter and its chromatin environment, the latter which entails specific nucleosome arrange-ments and histone tail modifications. There are different classes of core promoters, and a ques-tion that has not yet been addressed is whether certain types of core promoters are more suitable than others to propagate rhythmic TF binding to rhythmic transcriptional activities.
Core promoters often contain specific binding sites (core promoter elements) for general transcription factors (GTFs) such as TFIID and TFIIB [18,19]. GTFs together with RNA poly-merase II (Pol II) nucleate the pre-initiation complex (PIC), which assembles at the
for CLK:http://genesdev.cshlp.org/content/suppl/ 2011/11/11/25.22.2374.DC1/Abruzzi_SupTab1_ revised_final.xlsx. MNase-Seq and H2A.Z Chip-Seq reads for livers of WT and BMAL1−/−knockout mice:
GSE47142 and GSE47143, respectively. GRO-Seq mouse liver data: GSE59486. Drosophila S2 cell MNase-Seq, H2A.Z ChIP-Seq, and 3'NT-Seq data: GSE49106. TFIID-dependent transcripts: Supplementary Table 1 ofhttp://www.cell.com/cms/ attachment/597561/4683037/mmc1.pdf
Funding:Funding from the German Ministry of Science and Education (BMBF), FKZ 0315899, is hereby acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
transcription start site (TSS) before transcription can initiate. Different GTFs play different roles in PIC nucleation and initiation of transcription [20]. The TFIID subunit TATA-binding protein (TBP) binds to the TATA box, a core promoter element conserved from archaea to mammals, situated ~30 bp upstream of the TSS. The TFIIB subunit binds to BRE elements, located closely upstream (BREu) or downstream (BREd) of the TATA box, and helps recruiting Pol II. Other GTFs help opening DNA at the promoter to form a so-called transcription bub-ble, and also phosphorylate Pol II at its C-terminal domain and prime it for transcription. Vari-ous combinations of core promoter elements are thought to modulate the propagation of TF binding to transcriptional activation, in this manner helping to specify developmentally regu-lated or tissue specific transcription [21].
Eukaryotic core promoters are often thought to have an inactive ground state, assured by a tendency for nucleosomes to cover promoter DNA. In this ground state, the TSS is assumed to be covered by a nucleosome which constitutes a certain barrier for Pol II to penetrate [22–24]. Transcriptional activators recruit various chromatin remodeling factors, which facilitate PIC assembly and transcriptional initiation by loosening the nucleosomal barrier. This can happen through e.g. histone acetylation and ATP-dependent nucleosomal displacement, as shown for the human IFN-βpromoter [25]. One mode of circadian regulation of transcription could then be rhythmic binding and unbinding of transcription factors, resulting in rhythmic recruitment of histone modifiers and nucleosome remodelers that in turn free up the TSS for PIC formation and transcription initiation in a rhythmic fashion. Such a mode is indeed consistent with sev-eral hallmarks of the strongly circadianDbptranscript, including observed rhythmic CLOCK/ BMAL1 binding, histone H3 Lys4 trimethylation (H3K4me3) and Lys9 acetylation modifica-tions, as well as rhythmic gross H3 abundance at the TSS [26]. However, a subset of metazoan promoters appear to have an active ground state, in the sense that they are depleted of nucleo-somes and bound by Pol II even when there is no active transcription. A signature of such pro-moters is a high level of Pol II immediately downstream of the TSS (as detected by ChIP-Seq), compared to the Pol II level in the gene body [27,28]. The TSS to gene body Pol II ratio is referred to as pausing index, since Pol II has often already engaged in initial transcription at these promoters but sits paused ~50 bp downstream of the TSS. Paused Pol II is specifically detectable by techniques such as global run-on sequencing (GRO-Seq) as sharp peaks of nascent mRNA fragments aligning just downstream of the TSS [29]. There are regulated pro-cesses promoting such pausing, which involve pausing factors such as DSIF and NELF. Induc-tion of transcripInduc-tion at these TSSs involves pause release factors such as P-TEFb [30–32].
nucleosome-depleted regions, and ATP-dependent chromatin remodelers may elevate levels of the H2A.Z variant at the +1 nucleosome [47,48]. Although a debated issue,in vitroandin vivo
evidence collected to date suggest CpG dinucleotides help instruct the formation of nucleo-some-depleted regions of mammalian promoters [49–51]. Besides this DNA sequence feature, proteins associated with CpG-rich DNA as well as ATP-dependent chromatin remodelers probably help to establish nucleosome-depleted regions in vivo [46,50,52]. In particular, TSS-bound Pol II, perhaps in a paused state, could contribute to establishing nucleosome-depleted regions by sterically hindering nucleosome formation in a competitive fashion. This effect is probably more pronounced at nucleosome-depleted regions at CpG-poor promoters, with their higher propensity for nucleosome coverage [50,53].
Based on these myriad features and mechanisms, two main metazoan promoter classes, type I and type II promoters, have been proposed [54]. Type I promoters drive regulated, tissue-specific mRNA expression, have an inactive ground state with a less pronounced nucleosome-depleted region, are enriched for TATA boxes, and are depleted of CpG dinucleotides in vertebrates [55,56]. These promoters are often focused, which means that they have a well defined TSS, as measured with the CAGE assay [57]. Type II promoters drive ubiquitously expressed transcripts, have more pronounced nucleosome-depleted regions, are enriched for the H3K4me3 mark, CpG dinucleotides, and are depleted of TATA boxes. These promoters are often dispersed, meaning that they have multiple TSSs scattered over regions of tens or hundreds of bp [55,56]. They are also enriched for constitutively transcribed ("housekeeping") genes [41], presumably because their nucleosome-depleted regions are conducive to constitutive transcription.
Both type I and type II promoters may govern inducible transcription, however imple-mented in different manners, as suggested by two studies of LPS-induced transcription of ~50 genes in mouse macrophages [58,59]. Primary response genes, which are quickly induced with-out requirement for additional protein synthesis, tend to have type II promoters, and induction tends to occur by induced release of paused Pol II. Secondary response genes, which are more slowly induced with requirement for additional protein synthesis, tend to have type I promot-ers, require ATP-dependent chromatin remodeling, and to have an inactive basal state without bound Pol II with a nucleosome blocking PIC assembly. Open questions include whether circa-dian transcription–a particular mode of inducible expression–preferably employs type I or type II promoters, to which degree these rhythms are accompanied by oscillations on the asso-ciated chromatin hallmarks at the core promoters, and which mechanisms might be involved in circadian regulation of transcription at the core promoter.
This report describes the core promoters of genes transcribed in a circadian manner (hereaf-ter: circadian promoters) in mouse liver and inDrosophila, by integrating promoter sequence features (CpG ratios and core promoter elements) with a wealth of genome-wide data measur-ing chromatin state, transcriptional activities, and CTF bindmeasur-ing (Fig 1A). It is shown that the core promoter is of greatest importance for circadian transcription. In particular, a core pro-moter class in mouse combining hallmarks of type I and type II propro-moters is uncovered. This promoter class, here termed strong circadian promoter, drives circadian transcription with both high amplitudes and high average transcriptional rates.
Results
Core promoters of circadian genes are CpG-depleted and strongly
enriched for TATA boxes
these transcripts were estimated by reanalyzing Nascent-Seq data [11]. These data were col-lected from mouse livers sampled every 4 hrs under 12 hr/12 hr light-dark (LD) conditions (time points hereafter referred to as zeitgeber time, ZT). To avoid confounding effects from lighting conditions, only data obtained from mice held under LD were used in the present study. A group of 1895 promoters with very clear rhythmic transcriptional activities (Methods) were classified as circadian promoters. In contrast, a background group of 5829 promoters with significant transcriptional activities yet without any signs of circadian rhythms were clas-sified as constitutive promoters. A group of 4892 silent promoters without detectable or with very low transcriptional activities was also identified. For rhythmic activities, relative ampli-tudes (absolute ampliampli-tudes divided by means, hereafter: ampliampli-tudes) were estimated by har-monic regression (Methods) and used for the further analysis.
Fig 1. A. Overview of the study and the various data sources (listed in the callouts).Data were compiled from published studies of mouse liver in animals held under rhythmic light-dark (LD) conditions (green), or ofDrosophila(blue), or both (black). Rhythmic transcriptional activities were estimated from
Nascent-Seq data as visualized for theSaa1transcript. Rhythmic transcriptional activities thought to be generated by circadian transcription factors (CTFs) such as REV-ERBαandβ, E4BP4, and the CLOCK/ BMAL1 heterodimer in mouse, or CLK inDrosophila. The core promoter (beige box) mediates rhythms in CTF binding to rhythmic transcription initiation. This mediation may involve chromatin and nucleosomal remodeling, interacting with rhythms in general transcription factor and Pol II binding to core promoter elements such as the TATA box. B. Overview of promoter classification. Promoters were classified according to whether they drive circadian or constitutive transcription, according to core promoter sequence features, and according to detected CTF binding at the promoter.
To characterize core promoters, areas in the vicinities of the TSSs were scanned for the pres-ence of TATA boxes, BREu, and BREd motifs (Methods andS1A Fig). In addition, CpG ratios were quantified (Methods). Furthermore, MNase-Seq data [60], which quantify nucleosome occupancies, were reanalyzed. A summary statistic for nucleosome occupancy for each pro-moter was formed by averaging nucleosome coverages between−101 and−1 bp from the TSS.
Finally, mouse liver CAGE data, which measure precise TSS usage by transcripts [57], were used to classify promoters as focused or dispersed (Methods). Focused promoters have a well-defined TSS: Most transcripts start within a narrow range of a few bp. Dispersed promoters give rise to transcripts with starting positions varying by tens of bp. These three characteris-tics–core promoter sequence features and motifs, nucleosome occupancy, and TSS variation– have previously been used to characterize different promoter classes, and were used as a starting point for the present study.
Circadian promoters turned out to be enriched for TATA boxes when compared to consti-tutive promoters (Fig 2A, Fisher's exact test, p<5×10−7, odds ratio 1.54), although not as
strongly enriched as silent promoters. However, circadian promoters driving transcription with high amplitudes and high average activity were exceptionally strongly enriched for TATA boxes, with around 35% TATA box containing promoters (Fig 2A). Circadian promoters had a slightly less pronounced nucleosome-depleted region immediately upstream of the TSS than constitutive promoters (rank sum test, p<10−15, median ratio 1.24), but were significantly
more nucleosome-depleted than silent promoters (Fig 2B). There was a positive correlation between nucleosome occupancy immediately upstream of the TSS (hereafter: nucleosome
Fig 2. Basic properties of circadian promoters.A. Fractions promoters with TATA box for different
promoter classes, with 95% confidence intervals assuming binomial distributions. Legend: const. = constitutive; circ. = circadian; circ. HA & HT = circadian with high amplitude and high average transcriptional activity (upper quartiles, Methods). Bars from left to right:n= 5829, 1895, 4892, 116. B. Averaged
nucleosome occupancies for different promoter classes. Legend as in panel A. Position 0 refers to the TSS. Pileup refers to normalized MNase-Seq reads aligned to a given position. Median pileups for each position and promoter class are visualized. C. Transcriptional amplitude increases with nucleosome occupancy. Mean pileups for all circadian promoters at positions 101 to 1 bp upstream of the TSS were ranked and divided into 5 quantiles. Amplitudes in each such quantile are visualized (medians with 95% confidence intervals). D. Distribution of nucleosome occupancies immediately upstream of the TSSs of circadian promoters. Especially circadian promoters with TATA box appear to have a low occupancy subpopulation coexisting with a subpopulation with high nucleosome occupancies (arrow).
occupancy) and transcriptional amplitude (Spearman's rho = 0.28, p<10−15,Fig 2C). CpG
ratios were negatively correlated with transcriptional amplitudes (S1B and S1C Fig). Circadian promoters had overall slightly lower CpG ratios than constitutive promoters (rank sum test, p<10−13, median ratio 0.93), but silent promoters had much lower CpG ratios than circadian
promoters (S1D Fig). In agreement with these correlations and with previous studies [51,59], nucleosome occupancy was negatively correlated with CpG ratio (Spearman's rho =−0.45,
p<10−15S1E Fig).
Consistent with their enrichment for TATA boxes, circadian promoters also tended to be more focused than constitutive promoters according to the CAGE data (Fisher's exact test, p<0.002, odds ratio 1.24). Circadian TATA box promoters were furthermore depleted of the BREd motif compared to constitutive TATA box promoters (Fisher's exact test, p = 0.02, odds ratio 0.68), or when comparing circadian to constitutive promoters overall (Fisher's exact test, p<0.0033, odds ratio 0.85). No enrichment or depletion was detected for the BREu motif in circadian promoters compared with constitutive promoters (Fisher's exact test, p = 0.27, odds ratio 0.93). Thus, the BREd motif appears to favor constitutive mRNA expression.
Taken together, these findings seem to indicate that circadian promoters, especially those that drive transcriptional rhythms with high amplitude, tend to be of the type I class. However, the distribution of nucleosome occupancies for circadian promoters with TATA box was broad and bimodal (Fig 2D). Clearly, a large population of circadian promoters with TATA box had a nucleosome-depleted region upstream of the TSS, a hallmark thought to be more typical of type II promoters, while a group of other circadian promoters had much higher nucleosome occupancies (Fig 2D, arrow). As is the case in human cells [51,61], there was a general negative correlation between nucleosome occupancy and transcriptional activity among all promoters corresponding to expressed transcripts (Spearman's rho =−0.34, p<10−15,S1F Fig).
Consis-tent with this and the positive correlation between amplitude and nucleosome occupancy, there was among circadian promoters also a negative correlation between mean transcriptional activity and amplitude (Spearman's rho =−0.28, p<10−15). However, although circadian
pro-moters had higher nucleosome occupancy on average compared to constitutive propro-moters (Fig 2B), they did not have lower median transcriptional activity (rank sum test, p = 0.13, median ratio 1.03). Thus, circadian promoters might harbor separate promoter classes that are not detectable by averaging measurements over the entire promoter group. In particular, there might be a hidden group of circadian promoters with atypically high transcriptional activities, increasing the population average transcriptional activity of circadian promoters.
Identifying a separate class of strong circadian core promoters
As expected, circadian promoters were enriched for CTFs compared to constitutive promot-ers (Fisher's exact test, p<10−10, odds ratio 1.44). Further, CTF-binding circadian promoters
(n = 900) were enriched for TATA boxes, compared to circadian promoters not binding CTFs (n = 995, Fisher's exact test, p = 0.0017, odds ratio 1.56). Unexpectedly, however, CTF binding alone was not associated with high transcriptional amplitude (Fig 3A, left, rank sum test, p = 0.57, median ratio 0.99). Rather, TATA boxes and LCpG were associated with high tran-scriptional amplitudes, regardless of CTF binding (rank sum test, p<10−15, median ratio 1.4).
In contrast, CTF binding was strongly associated with high average transcriptional activities
Fig 3. Identifying strong circadian promoters (SCPs).A. Amplitudes and averages of transcriptional activities as measured by Nascent-Seq were quantified for the transcript corresponding to each promoter. Promoters were stratified as either TATA box-containing and/or LCpG, or TATA-less and HCpG, as well as exhibiting either no, or at least one CTF ChIP-Seq peaks (CTFs, circadian transcription factors BMAL1, REV-ERBα, REV-ERBβ, and E4BP4). TATA boxes and LCpG were strongly associated with high circadian
transcriptional amplitudes, while CTFs rather were associated with high average transcriptional activities. Box plots show medians, 25/75% quantiles, and minimum/maximum values. Promoter groups from top to bottom:n= 665, 605, 330, 295. B. Nucleosome occupancies 101 to 1 bp upstream of the TSSs for SCPs and type I circadian promoters (with TATA box or LCpG but not CTF binding). Occupancies were computed from MNase-Seq data (Methods). Data are presented as kernel densities. C. Nucleosome occupancies around TSSs for different promoter classes. Pileups were computed from MNase-Seq data (Methods) and averaged over the promoter classes for each position relative to the TSS (excluding top and bottom 1% values, respectively, due to a few outlier promoters). Circ. = Circadian; Const. = Constitutive; Type I defined as in panel B, type II = CTF binding TATA-less and with HCpG. D. Fraction of TFIID-dependent promoters for different classes of circadian promoters, with 95% confidence intervals assuming binomial distributions. Bars from left to right:n= 721, 330, 605, 78, 160, 57. E. Mature mRNA expression levels and transcript half lives for different promoter classes. Mature mRNA expression levels were quantified from poly(A)+RNA-Seq data
for the transcript corresponding to each promoter; transcript half lives were compiled from two literature sources (Methods). Mean expression levels and half lives were median averaged over the promoter classes and are shown as points. Error bars represent 95% confidence intervals for the medians. Circ. = Circadian; Const. = Constitutive; TATA = with TATA box; type I and type II as in panels B and C.
(Fig 3A, right, rank sum test, p<10−15, median ratio 2.4), independently of TATA boxes or
LCpG. These effects were additive: Promoters with both CTF binding and a TATA box or LCpG were associated with very strong transcription, with high amplitudes as well. Note that the scale is logarithmic and that the fold differences in mean transcriptional activities are quite large. The same effects were observed when stratifying for TATA boxes or CpG content separately (S2A Fig).
CTF-binding circadian promoters with either LCpG or TATA box are hereafter referred to as strong circadian promoters (SCPs, listed inS1 Table), in light of the marked association of these sequence features with both strongly rhythmic and high transcriptional activities.
SCPs have distinct, atypical combinations of core promoter features
To investigate and characterize SCPs further, a search for distinguishing characteristics of these promoters in terms of nucleosome occupancy and sequence features was carried out. By stratifying all circadian promoters with LCpG or TATA box into either SCPs or non-CTF bind-ing promoters (Fig 1B), the broad bimodal nucleosome occupancy distribution (Fig 2F) was resolved into the population of SCPs, which had low nucleosome occupancy, and the popula-tion of non-CTF binding LCpG or TATA box containing circadian promoters, which had high nucleosome occupancy (Fig 3B). In stark contrast to the general anti-correlation between pro-moter nucleosome occupancy and CpG ratio, SCPs turned out combine low average CpG ratios with low nucleosome occupancies (S2B Fig). SCPs had, in fact, almost as low nucleosome occupancy as constitutive TATA-less HCpG promoters without CTF peaks (hereafter: consti-tutive type II; Figs1Band3C). Thus, SCPs appeared to escape both the negative correlation between amplitude and mean transcriptional activity, as well as between amplitude and nucleo-some depletion (Fig 2C). The group of circadian promoters that do not bind CTFs, but with LCpG or TATA box, on the other hand, had characteristics of canonical type I promoters [54]: low median transcriptional activities (Fig 3A) and high nucleosome coverage (Fig 3C). Promot-ers in this group are hereafter referred to as circadian type I promotPromot-ers (Fig 1B).
Circadian CTF binding HCpG promoters without TATA box, on the other hand, had the low nucleosome occupancies typical of type II promoters (Fig 3C), which given the initial anal-ysis here were consistent with their low oscillation amplitudes (Fig 3A). These promoters are hereafter called circadian type II promoters (Fig 1B).
For comparison to earlier results on correlations between core promoter properties and nucleosome occupancies [55,56], constitutive promoters with LCpG or TATA box (but with-out CTF peaks, to exclude residual circadian promoters whose transcriptional rhythms might not have been detected), were also investigated. These promoters (hereafter: constitutive type I promoters,Fig 1B) had the expected high nucleosome occupancies upstream of the TSS, whereas constitutive type II promoters tended to have the familiar pronounced nucleosome-depleted region upstream of the TSS (Fig 3C).
One possibility would be that SCPs simply constitute regular type I promoters that happen to be very strongly transcribed. To exclude this, constitutive type I promoters were sampled (with replacement) to match the distribution of average nascent mRNA read counts of SCPs. This resulted in an artificial group of constitutive TATA box promoters with the same statisti-cal distribution of transcriptional activities as SCPs. A bootstrapping test procedure was then employed to probe whether nucleosome occupancies of the matched constitutive type I pro-moter population are similar or different to those of SCPs (Methods). This analysis (S2 Table) showed that SCPs indeed have significantly lower nucleosome occupancy than constitutive type I promoters with matched mean transcriptional levels.
MNase-Seq data from mouse livers of Bmal1−/−animals [60] yielded nucleosome profiles very
similar to those of livers from wild type mice (S2C Fig). This points to other reasons than BMAL1 for the nucleosome depletion immediately upstream to the TSS of SCPs.
Does daily up- and downregulation of transcription require mechanisms associated with focused transcription initiation? As mentioned, regulated and CpG-poor TATA
box containing promoters are often focused. However, promoters with an ordered nucleosome pattern and a pronounced nucleosome-depleted region upstream of the TSS are generally more dispersed [54]. This relationship between nucleosome coverage and TSS width could be repro-duced with the present data set: dispersed promoters had significantly lower nucleosome occu-pancy (rank sum test, p<10−15, median ratio 0.81). Thus, since SCPs are CpG-poor and
enriched for TATA boxes, but on the other hand have low nucleosome occupancies, it is not obvious if SCPs should tend to be focused or dispersed: the general rules are in conflict. An analysis showed that SCPs with TATA box were mainly focused with a smaller dispersed sub-population, but that TATA-less SCPs (consequently with LCpG) tended to be much more dis-persed (S2D Fig). Thus, strong circadian transcriptional rhythms appear compatible both with focused and dispersed transcriptional initiation.
Since differentiated cells often do not employ TFIID for the majority of transcripts, it is of interest to assess whether SCPs, circadian promoters, or even expressed transcripts in general are associated with this PIC subunit in the adult mouse liver. To do this, the lists of expressed promoters, circadian promoters, and SCPs were cross-referenced with a list of transcripts that are down-regulated in mice with a liver-specific deletion of the TAF10 subunit of TFIID, with-out which TFIID dissociates [62].
There was a remarkable enrichment for such TFIID-dependent transcripts among transcripts with promoters of expressed transcripts binding CTFs, especially CTF-binding promoters with TATA box. In fact, around 89% of all 130 TFIID-dependent expressed transcripts had CTF bind-ing promoters (S2E Fig), compared to 41% for non-TFIID-dependent expressed transcripts (nei-ther up-regulated nor down-regulated upon TAF10 depletion, Fisher's exact test, p<10−15, odds
ratio 12.2). Conversely, focusing on circadian promoters, CTF binding in conjunction with LCpG was associated with TFIID-dependence. 12% of LCpG SCPs were strongly TFIID-depen-dent, compared to 0.03% for circadian promoters with neither CTF binding nor TATA box or LCpG (Fig 3D). A possible caveat might be that the observed down-regulation thought to be due to TFIID dissociation in fact was due to circadian transcription: samples were not necessarily col-lected at the same times of day. However, then an overrepresentation of transcripts up-regulated upon TAF10 depletion would also be expected among CTF binding promoters if all phases are roughly equally probable. Such an overrepresentation was not found among the 119 expressed transcripts up-regulated upon TAF10 depletion (48% vs. 41%, Fisher's exact test, p = 0.11, odds ratio 1.35). This suggests that the down-regulation likely indeed is due to TFIID dissociation. These results suggest that CTF binding promoters, in particular those with LCpG, tend to employ TFIID also in fully differentiated hepatocytes to a higher degree than other promoters.
The SCPs appeared to drive extraordinarily high transcriptional activities and amplitudes (Fig 3A). Is this reflected in mature transcript abundances as well? By analyzing mouse liver mature mRNA abundances from RNA-Seq measurements over 2 circadian cycles [11], match-ing these to the Nascent-Seq transcripts, and estimatmatch-ing mean mRNA expression levels and oscillation amplitudes (Methods), this question could be answered. As shown inFig 3E, the extraordinarily high mean expression levels and amplitudes of SCPs were indeed carried over to the mature transcript levels. These results were confirmed using an alternative microarray-based data set to quantify mature mRNA abundances [63] (Methods andS2F Fig).
counteracted by high transcriptional activities in the case of mRNAs. Cross-referencing the data on mRNA amplitudes with data on mRNA half lives [64,65] showed that mRNAs driven by SCPs with TATA box have extraordinarily short half lives compared to any other class of mRNAs, including constitutively expressed mRNAs driven by either TATA box or TATA-less promoters (Fig 3E). The high mRNA abundances associated with TATA box SCPs are thus reached in spite of short transcript half lives, presumably through their exceptionally high tran-scriptional activities.
In summary, the results suggest that SCPs might constitute a separate core promoter class. SCPs are characterized by being much more strongly transcribed and by having much lower nucleosome occupancies than standard type I promoters, yet they retain low CpG ratios.
Exceptionally high and rhythmic paused Pol II levels, but constant and
low nucleosome occupancies at SCPs
What may cause the nucleosome depletion upstream of the TSSs of SCPs? It might be the case that promoter-proximal paused Pol II, or associated factors, hinder nucleosome assembly at these promoters [53]. To follow up on this hypothesis here, ChIP-Seq data on Pol II in mouse liver sampled at 7 time points over 24 hours [10] were reanalyzed. Sequencing reads were com-piled both for the promoter-proximal pausing region (20–100 bp downstream of the TSS) as well as for the gene body (300–1300 bp downstream of the TSS), which yielded normalized RPKM values for these regions, reflecting the amount of bound Pol II, or Pol II coverage (Methods). This also allowed calculating the pausing index (PI), which is the Pol II coverage in the promoter-proximal pausing region normalized by the gene body Pol II coverage. The PI reflects the tendency of Pol II to remain associated with the TSS for a given level of transcrip-tional activity. To verify that this is accompanied by truly paused Pol II, the analysis was com-plemented with GRO-Seq data sampled at 8 time points over 24 hours in duplicates [6], in order to detect peaks of short transcripts produced by paused Pol II.
Indeed, SCPs had higher promoter-proximal Pol II coverage than any other promoter group, including circadian or constitutive type II promoters, even though the latter two pro-moter groups had slightly lower nucleosome occupancies as SCPs (Fig 4A). These high Pol II levels at SCPs were accompanied by an even stronger peak of GRO-Seq reads (S3A Fig), sug-gesting that Pol II sits paused but engaged in transcription at these promoters. The bootstrap-ping test procedure for comparing SCPs to transcriptional activity-matched constitutive type I promoters (above and Methods) showed that this high Pol II coverage is indeed much more common among SCPs as compared to highly expressed type I promoters (p<10−6).
Although there also was a general negative correlation between nucleosome occupancy and paused Pol II, SCPs consistently displayed atypically high levels of paused Pol II for a given nucleosome occupancy (S3B Fig), but not atypically high PIs (S3C Fig). The lower PIs may be attributed to SCPs being associated with higher Pol II gene body levels when compared to other circadian promoters (rank sum test, p<10−15, median ratio 2.3,Fig 4A). This is
consis-tent with the idea that especially SCPs might employ Pol II to maintain a low nucleosome occu-pancy. It also suggests that Pol II sits mainly in a paused state at SCPs, ready to initiate
transcription at appropriate time windows.
occupancies and Pol II levels. The sequencing depth of the MNase-Seq data is too low to allow reliable quantification at the single-promoter level; they are, however, readily analyzed at the promoter population level. For this, promoters were binned according to their transcriptional phase as determined by analysis of the Nascent-Seq data (Methods), plus/minus 2 hrs. The MNase-Seq data were reordered temporally so that for each promoter, time point 0 corre-sponded to the transcriptional phase of the promoter's bin. The same exercise was performed Fig 4. Pol II, nucleosome, and H3K4me3 dynamics at circadian promoters.A. Pol II levels around TSSs for different promoter classes. Pileups were computed from ChIP-Seq data (Methods) and averaged over the promoter classes for each position relative to the TSS. Circ. = Circadian; Const. = Constitutive; NC = non-TATA box, CTF binding; NN = non-TATA box, non-CTF binding. B. Time-resolved pileups of
nucleosome occupancies (MNase-Seq data, methods), and Pol II levels (Pol II ChIP-Seq data, Methods) around the TSSs, averaged over all SCP promoters, phase-shifted according to transcriptional phase. Dot plots represents averages over positions 41 to 121 bp downstream of the TSS (nucleosomes), and positions 51 to 151 bp downstream of the TSS (Pol II). The Pol II levels depicted in the dot plot were rhythmic (harmonic regression p = 0.01), but the nucleosome occupancies were not (harmonic regression p = 0.52). C. Summary of the kinetic model analyzed. The model includes recruitment, initiation, and pausing release steps with accompanying rate constants. Increased Pol II recruitment rate only leads to both increased paused Pol II levels and increased rhythm propagation, if combined with increased Pol II initiation rates and decreased Pol II pause release rate constants. D. Transcriptional amplitudes decrease with higher H2A.Z levels. Mean H2A.Z pileups for all circadian promoters at the +1 nucleosome normalized to the +1 nucleosome peak (Methods) were ranked and divided into 5 quantiles for visualization. Medians and 95% confidence intervals of amplitudes in each such quantile are shown. E. H4K3me3 levels around TSSs for different promoter classes. Pileups were computed from ChIP-Seq data (Methods) and averaged over the promoter classes for each position relative to the TSS (excluding top and bottom 1% values, respectively, due to a few outlier promoters). Circ. = circadian; Const. = constitutive. NC = non-TATA box, CTF binding; NN = non-TATA box, non-CTF binding. Arrow highlights the striking high H3K4me3 signal continuing into the gene body.
with the Pol II ChIP-Seq data, in order to compare the results properly. Visualizing the result-ing 6 phase bins revealed a distinctly static nucleosome landscape around the TSSs of SCPs (Fig 4B, p = 0.52, harmonic regression test for rhythmicity). On the other hand, Pol II levels, analyzed in the same manner, revealed a clear rhythmicity that coincided with the transcrip-tional phase (Fig 4B, p = 0.01, harmonic regression test for rhythmicity). The lack of nucleo-some occupancy rhythms was pervasive also at circadian promoters other than SCPs, and the promoter-proximal Pol II levels had weaker rhythms at these promoters (S3D Fig). Hence, the present analysis showed no evidence that nucleosomal dislocation is commonly employed for generating transcriptional circadian rhythms. However, at SCPs, rhythmic promoter-proximal Pol II levels may reflect an involvement in rhythmic transcriptional regulation. These Pol II rhythms had a very high base line, which might serve to keep these promoters permanently nucleosome-depleted, enabling a high average rate of transcriptional initiation.
How may the strong tendency for promoter-proximal Pol II accumulation be achieved and combined with generation of transcriptional rhythms? To investigate this from a biochemical kinetics perspective, a simple but general mathematical model formulated previously [10] was considered. The model describes Pol II recruitment, transcriptional initiation, and release of Pol II into productive elongation, allowing rhythmicity both in recruitment and release rates (Fig 4C). A rigorous analytical and parameter-independent reanalysis of the model (S1andS2
Texts,S1 Interactive Text) showed that two scenarios–a higher average Pol II recruitment rate, or a lowered release rate–could both explain the increased Pol II promoter-proximal pausing at SCPs compared to e.g. circadian type II promoters. The lower PI could in both these cases be explained by additionally assuming decreased transcriptional elongation speeds causing higher Pol II density (note that not transcriptional speeds, but rather transcriptional initiation rates determine how many transcripts are produced per time unit [66]). The first scenario, higher average Pol II recruitment rates, in theory generally comes with weaker combined net propaga-tion of rhythms in Pol II recruitment and Pol II release rates to transcrippropaga-tional activities (Fig 4C,S2 Text,S1 Interactive Text). Checking this prediction against data indeed revealed a nega-tive correlation between PI and transcriptional amplitude (Spearman's rho =−0.30, p<10−15).
However, the assumption of only higher average Pol II recruitment rates for SCPs is not an attractive explanation for their high Pol II signals in the promoter-proximal regions, since SCPs drive strong rhythms. Instead, the situation for SCPs is better explained by invoking the alternative scenario (Fig 4C): Additionally assuming lowered average Pol II release rates also help explain the higher promoter-proximal Pol II signals of SCPs. This would be consistent with a tendency at SCPs for shifting of rhythmic control from Pol II recruitment to Pol II release, with retained overall rhythm propagation, that could be inferred from the data at hand (S1andS2Texts,S1 Interactive Text,S3E and S3F Fig).
If this is true, additionally a higher Pol II initiation rate (from recruited to paused Pol II) must be assumed to explain the high transcriptional activities of SCPs (S1andS2Texts,S1 Interactive Text). Such high initiation rates have indeed been found to be prevalent for TATA box promoters, partly due to a transcription initiation scaffold being constitutively bound to TATA box promoters of active genes [67]. A combination of much higher initiation rates and lower release rates would be consistent with all data: SCPs having higher promoter-proximal Pol II signal, lower PI, higher transcriptional rates, and stronger rhythms.
Histone variant H2A.Z disfavors circadian rhythms
activation via various mechanisms [48]. In general, nucleosomes with the H2A.Z variant are more unstable, and the variant is associated with nucleosome-depleted regions [68,69], although the causal relationships behind this association are unclear [48]. Certain studies have also established a negative correlation between paused Pol II occupancy and H2A.Z. Possibly, H2A.Z at the first (+1) nucleosome downstream of the TSS facilitates the elongation of Pol II through the +1 nucleosome position [70]. If this is true, and if the average Pol II release rates of SCPs are indeed lower than for circadian type II promoters, then SCPs would have lower H2A. Z levels. If H2A.Z is associated with transcriptional activation, periodic up- and down-regula-tion might be incompatible with high H2A.Z levels. Hence, another quesdown-regula-tion of more general nature is whether circadian transcriptional regulation is associated with H2A.Z depletion.
To address this, mouse liver H2A.Z ChIP-Seq data were reanalyzed. For all mouse promot-ers, the H2A.Z signal around the +1 nucleosome (determined from the MNase-Seq data with a peak detection algorithm, Methods) was computed, and normalized to the MNase-Seq based nucleosome signals. Indeed, there was a negative correlation between H2A.Z signal and levels of promoter-proximal paused Pol II for promoters with marked levels of paused Pol II (S3G Fig). In particular, SCPs were characterized by low H2A.Z levels and correspondingly high pro-moter-proximal paused Pol II levels. As was the case for Pol II coverage, the bootstrapping test procedure (Methods) showed that SCPs also have lower H2A.Z levels than highly expressed type I promoters (p<10−6).
This is consistent with the model prediction that SCPs have lower Pol II release rates, given the notion that H2A.Z facilitates transcription through the +1 nucleosome. Depletion of H2A.Z might thus enable tighter regulation of Pol II release, exploited at promoters of genes transcribed in a strongly circadian manner. In line with the idea that H2A.Z might be more associated with constitutive transcription, there was a negative correlation in general between circadian tran-scriptional amplitudes and H2A.Z levels (Fig 4D, Spearman's rho =−0.28, p<10−15).
An H3K4me3 pattern extending into the gene bodies characterizes
SCPs
The H3K4me3 modification is strongly associated with active transcription and has been implicated in generation of circadian rhythms in certain transcripts [26,71]. H3K4me3 is fur-thermore associated with high levels of Pol II in the promoter-proximal region [28], and might thus be a hallmark of SCPs. Inconsistent with this, however, is an observed positive correlation between H3K4me3 and H2A.Z levels [68]. Thus, there are reasons both to expect high as well as moderate H3K4me3 levels at SCPs. H3K4me3 levels of mouse liver promoters at different ZT have been analyzed by ChIP-Seq [10]. Although H3K4me3 levels oscillate at the promoters of many genes transcribed in a circadian fashion, the oscillations generally lag the transcrip-tional rhythms by several hours. Thus, rhythms in H3K4me3 levels near promoters might be consequences rather than causes of rhythmic transcription. This situation could, however, vary between promoter classes, although this possibility has yet not been investigated.
In order to clarify the role of H3K4me3 at SCPs and other promoter classes, the H3K4me3 ChIP-Seq data [10] were reanalyzed and quantified at different locations of the mouse promot-ers. This analysis showed that SCPs had significantly lower levels of H3K4me3 just down-stream of the TSSs than circadian type II CTF binding promoters (Fig 4E, rank sum test, p = 3.8×10−7, median ratio 0.67). The lower H3K4me3 levels of SCPs apparently occur despite
the TSS H3K4me3 levels and P/M ratios of SCPs indeed are significantly lower and higher, respectively, than for highly expressed type I promoters (p<10−6in both cases). This
relation-ship apparently runs counter to the previously observed positive association between the H3K4me3 mark and promoter-proximal Pol II levels [28], which was reproduced with the present data set (S3H Fig). For a given promoter-proximal Pol II level, SCPs had atypically low H3K4me3 levels at the TSS. This does not mean that SCPs had very low H3K4me3 levels; in fact they were intermediate to high (Figs4EandS3H), but significantly lower than expected given their extraordinarily strong promoter-proximal Pol II signals. Particularly notable, how-ever, was the marked extension of the H3K4me3 mark far into the gene bodies of SCPs (Fig 4E), so that from a position of +500 bp of the TSS and onwards, genes with SCPs had the high-est gene body H3K4me3 level of any group of genes, which was also verified by the bootstrap-ping test against expression-matched constitutive type I promoters (p<10−6). There was also
a general negative correlation between average TSS H3K4me3 levels and circadian transcrip-tional amplitudes (Spearman's rho =−0.37, p<10−15), and consistent with this and the results
regarding H2A.Z and amplitudes outlined above, the previously observed correlation between H2A.Z and H3K4me3 levels [68] was notable also in the present data set (S3I Fig). This sug-gests that although SCPs had intermediate to relatively high H3K4me3 levels, extremely high levels of the H3K4me3 mark do not go hand in hand with strong circadian rhythms. Previ-ously, high H3K4me3 levels have been suggested to co-occur almost exclusively with high CpG ratios [45,54]. Although this correlation was evident also in the present data, SCPs constituted a notable exception, combining intermediate to relatively high H3K4me3 levels with low CpG ratios (S3J Fig).
A possible interpretation of these observations is that the observed intermediate H3K4me3 levels of SCPs reflect Pol II-mediated H3K4 trimethylation, pushing H3K4me3 levels up to intermediate rather than low levels. This intermediate level might be low enough to avoid a constitutively permissive chromatin state that would weaken rhythmic regulation. A non-causal role of H3K4me3 in circadian transcriptional activities has also been proposed to account for the later phases of rhythmic H3K4me3 levels with respect to transcriptional activi-ties [10]. This would be consistent too with the high H3K4me3 levels in the gene body, since transcriptional elongation speed in the present analysis was predicted to be slower for genes with SCP promoters (see above), which could lead to the observed higher gene body Pol II sig-nals. Indeed, the later phases of H3K4me3 signal compared to transcriptional activities were readily reproducible (S3K Fig) for CTF-binding promoters (including SCPs). However, this analysis revealed that rhythmic H3K4me3 levels of non-CTF binding circadian promoters tended to have earlier phases than those of transcriptional activities (S3K Fig). This opens up the possibility that H3K4 trimethylation plays a causal role in rhythmic transcriptional activa-tion mediated by other TFs than CTFs.
Shared characteristics of
Drosophila
and mouse circadian core
promoters, but no
Drosophila
SCP class
The results reported so far established that mouse liver circadian promoters are strongly enriched for TATA box promoters, and further uncovered a special class of promoters: SCPs. These circadian promoter traits could be specific for mammals, or they could represent more universal constraints on core promoters for circadian regulation of transcription. A compari-son with an evolutionary distant animal may throw light on the universality of circadian core promoters.
clock were compiled, resulting in a data set that, bar tissue-specificity, almost rivaled the data compiled for mouse liver. Time-resolved nascent and mature RNA-sequencing [12], and ChIP-Seq measurements for CLK binding sites [8] of fly head mRNA samples, nucleosome occupancies (MNase-Seq as well as H2A.Z ChIP-Seq), and transcribing Pol II positions in S2 cells (3' end nascent transcripts-sequencing, 3'NT-Seq) [70] as well as CAGE data on TSS usage in fly embryos [72] were compiled, using the RefSeqDrosophilatranscript annotation (Methods). As was the case for mouse liver,Drosophilacircadian transcripts turned out to be enriched for TATA boxes, compared to constitutive transcripts, especially transcripts with high amplitudes (Fig 5A, p = 0.035 and 0.00061, respectively, Fisher's exact test). Moreover, the presence of a TATA box positively correlated with amplitude but not mean mRNA expression levels, whereas CLK binding to the promoter region correlated with mean mRNA expression level but not amplitude (Fig 5B). These relationships are analogous to what was found for TATA box and CTF binding, respectively, in mouse liver. Reinforcing the analogy to mouse TATA box SCPs, circadian promoters were overall depleted of broad TSSs (Fisher's exact test, p = 0.0011, odds ratio 0.56). As for mouse liver, there was also a positive correlation between circadian amplitude and nucleosome occupancy immediately upstream of the TSS, as well as a negative correlation between transcriptional amplitude and H2A.Z content at the first nucleo-some downstream of the TSS (Fig 5C, Spearman's rho = 0.44, p<10−12for nucleosome
occu-pancy, Spearman's rho =−0.33, p = 1.2×10−7for H2A.Z).
However, there were only 2 promoters both containing a TATA box as well as binding CLK. In fact, there was no evidence of a promoter class corresponding to mouse SCPs. Circadian promoters inDrosophilagenerally had high nucleosome occupancies (except CLK-binding promoters,Fig 5D), even circadian TATA box promoters driving strong average transcrip-tional rates (S4A Fig). Further,Drosophilacircadian promoters driving high transcriptional amplitudes were depleted of paused Pol II compared to circadian promoters driving low tran-scriptional amplitudes (rank sum test, p = 8.8×10−9, median ratio 0.25). There was a negative
correlation between paused Pol II and nucleosome occupancy immediately upstream of the TSSs of expressed transcripts (Spearman's rho =−0.20, p<10−15), as previously described
[30], which implicates competition between Pol II and nucleosome occupancy. However, there was no apparent SCP-like subpopulation of promoters with high amounts of paused Pol II and low nucleosome occupancy, at the same time also driving transcriptional rhythms with both high amplitudes and mean levels (S4A–S4D Fig).
These results suggest that the following traits: 1. high nucleosome occupancies, 2. low Pol II pausing levels, 3. depletion of the H2A.Z variant downstream of the TSS, and 4. a TATA box-based promoter architecture, are universally correlated to circadian regulation of transcription, albeit with moderate average mRNA expression levels. The evolution of SCPs–TATA
box promoters with high Pol II pausing levels and low nucleosome occupancies that drive rhythms with both high averages and amplitudes–seems to not have occurred inDrosophila.
Discussion
altered average levels. By targeting the core promoter, e.g. the TATA box, by gene editing tech-niques [73,74], it might be possible to more precisely attenuate rhythmicity while keeping aver-age levels constant.
Two major classes of circadian promoters emerged in the analysis. The most peculiar of these represented strong circadian promoters (SCPs), driving circadian transcription with both Fig 5. Properties ofDrosophilacircadian promoters.A. Fractions promoters with TATA box for different promoter classes. Legend: Const. = constitutive; Circ. = circadian; Circ. HA = circadian with high amplitude (upper 25% quantile). From left to right: n = 5134, 247, 1541, 62. B. Amplitudes and averages of
transcriptional activities as measured by Nascent-Seq were quantified for the transcript corresponding to each promoter. Promoters were stratified as TATA box-containing and TATA-less (top), or as exhibiting either no, or at least one CLK ChIP-Seq peak (bottom). Medians and 95% confidence intervals are shown. From top to bottom, left to right: p = 0.0023, p = 0.16, p = 0.29, p = 0.066, rank sum tests. The last p value (mean for CLK vs. no CLK) became much smaller when considering all expressed transcripts: p = 2.5×10−17, median
ratio 1.4 CLK vs. no CLK (S3 Table). C. Amplitude increases with nucleosome occupancy, and decreases with levels of nucleosome variant H2A.Z. Mean nucleosome pileups at positions 101 to 1 bp upstream of the TSS, and normalized H2A.Z levels at the +1 nucleosome peak, respectively, for all circadian promoters were ranked and divided into 4 quantiles. Amplitudes in each such quantile are visualized. D. Nucleosome occupancies around TSSs for differentDrosophilapromoter classes. Pileups were computed from MNase-Seq data (Methods) and averaged over the promoter classes for each position relative to the TSS (excluding top and bottom 1% values, respectively, due to a few outlier promoters). Circ. = circadian; Const. =
constitutive.
high amplitudes and high average rates. SCPs appear to represent a class of promoters that has not previously been characterized, combining traits of type I and type II core promoter classes as commonly defined [54], and combining circadian regulation of Pol II recruitment and pause release. With regard to chromatin state, a defining signature of SCPs was a high paused Pol II level combined with a low H3K4me3 level relative to the high Pol II level. On the other hand, SCPs had H3K4me3 levels extending further into the gene bodies than other promoters. Finally, although SCPs had low CpG ratios, they had marked nucleosome-depleted regions upstream of the TSSs. The second major circadian promoter class consists of type I promoters, which exhibited moderate average transcriptional activities and high nucleosome occupancies upstream of the TSSs.
The important role played by the core promoter may help explain why only some CTF binding promoters drive circadian transcription. The E-box is strongly associated with the gen-eration of circadian rhythms in mammals andDrosophilasince it provides a binding platform for circadian TFs CLOCK/BMAL1 and CLK/CYC, respectively. However, the E-box was origi-nally discovered in the context of constitutive or transient activation of transcription, affecting processes involved in differentiation and development [75]. Thus, the E-box is presumably also implicated in non-rhythmic activation of target gene transcription. In the same way, ROR ele-ments, which are the DNA recognition sites for nuclear receptors REV-ERBαandβ, probably are able to bind other, non-rhythmic TFs of the nuclear receptor family, directing transient or constitutive rather than rhythmic transcription [76,77]. What decides whether a given CTF recognition site and promoter/transcript are associated with actual CTF binding and rhythmic transcription? The present results suggest that sequence features of the core promoter, such as TATA boxes, provide one piece of this puzzle. In particular, among circadian promoters, TATA boxes and low CpG content were more predictive of strong rhythms than CTF binding per se. Interestingly, a recent study found rather limited effects of BMAL1 deletion on CCG transcriptional rhythmicity in mouse liver [78].
What properties of the TATA box make it suitable for circadian regulation of transcription? TATA boxes can facilitate the assembly of a transcription reinitiation scaffold, consisting of TFIIA, TFIID, TFIIE, TFIIH, and Mediator, which remains on the promoter after Pol II pro-moter escape and makes rapid reassembly of the PIC possible [67,79]. Such reinitiation may not be unconditional, since the scaffold does not contain TFIIB and TFIIF, and since it is stabi-lized by TFs [79,80]. This scaffold mechanism involves TFIID, and although differentiated tis-sues in mammals do not always rely on TFIID for PIC formation [20,81], there is a subgroup of promoters in mouse liver that does show a strong dependence on TFIID for transcription [62]. Remarkably, CTF binding promoters and SCPs in particular accounted for around 89% of these TFIID-dependent promoters (S2E Fig). Thus, TATA box promoters and PIC scaffolds may constitute a suitable platform for strong but rhythmically gated transcription: in this sce-nario, circadian activators or repressors recruit TFIIB and Pol II in a rhythmic fashion to per-manently assembled scaffolds.
relationships between the H3K4me3 mark and transcriptional activities [87]. Pol II can attract histone methylases leading to H3K4 trimethylation [42], but clearly, since SCPs had higher promoter-proximal Pol II than any other promoter class but lower H3K4me3 levels than circa-dian or constitutive type II promoters, other mechanisms must be at work. SCPs are CpG-poor, which may be a reason for their relative H3K4me3 depletion, since CpG dinucleotides may induce H3K4 trimethylation via CFP1 [45].
Why are promoters driving high amplitude circadian transcription (SCPs and circadian type I promoters) depleted of H3K4me3, relatively seen? Perhaps high levels of these nucleo-some features should be viewed as hallmarks of high constitutive (or perhaps poised) transcrip-tion [32]. Circadian promoters need to not only rhythmically induce transcription, but also repress it, and possibly, slow kinetics of H3K4me3 demethylation precludes timely clearance of this histone mark at some promoters, so that they cannot drive circadian rhythms [10,58]. On the other hand, this might not be the case universally, since circadian type I promoters exhib-ited rhythmic H3K4me3 levels with earlier phases than transcription (S3K Fig), possibly indi-cating a causal role for this histone mark, as has also been demonstrated for certain genes [71]. Such a causal role may involve H3K4me3-mediated TFIID recruitment [43]. Assuming this scenario, a prediction is that H3K4me3 demethylation is only slow or inefficient enough to pre-clude circadian regulation when Pol II levels are constitutively high, perhaps persistently induc-ing H3K4 (re-)methylation. Finally, the extension of the H3K4me3 mark into the gene bodies of SCPs could be a by-product of high Pol II densities due to high rates of transcription initia-tion. This would also be in line with recent observations made by other investigators [88].
As was the case for the H3K4me3 mark, high levels of the H2A.Z nucleosome variant were also associated with lower amplitudes. These two observations are probably related, since the degree of correlation between the H3K4me3 mark and H2A.Z levels was notable, which cor-roborates earlier findings [38,68]. The association of H2A.Z with low amplitudes entails the prediction that regulation of H2A.Z levels at the time scale of circadian rhythms in general is not fast or reliable enough to be exploited for rhythm generation.
This may be the case for circadian nucleosomal occupancy dynamics in general. There are certainly cases where induced transcription involves nucleosome rearrangement [26,59,89]. How-ever, the MNase-Seq data analyzed here provided no evidence at the population level of wide-spread rhythmic nucleosome occupancies at the promoters of circadian transcripts (sequencing depth was not high enough to reliably assess rhythms in nucleosome occupancy at single promot-ers). Absence of evidence is not evidence of absence, but notable is that rhythmic nucleosome occupancies at the population level was readily detectable at BMAL1 binding sites [60]. Thus, the data suggest that circadian core promoters have a relatively fixed nucleosome occupancy deter-mined by factors not varying at the circadian time scale. This does not mean that nucleosome occupancy has nothing to do with transcriptional activities: nucleosome occupancies were nega-tively correlated to average transcriptional activities in a continuous fashion (S1F Fig). Such cor-relations are also present in yeast [90] and mouse and human embryonic stem cells [91].
Importantly, observed averages in tissue samples translate to probabilities of nucleosome occupancy in the single cell. There, nucleosomal occupancy is a dynamic phenomenon, where nucleosome sliding, binding and unbinding continuously takes place [92–94]. It thus makes sense that transcriptional activity varies continuously with nucleosome occupancy as observed at the tissue level, rather than being an on/off phenomenon. The average nucleosome occupan-cies may be set by a combination of sequence-determined nucleosome forming potential and auxiliary proteins [46,52]. However, these features do apparently not represent activities that vary in a circadian manner.
However, only circadian type I promoters had high nucleosome occupancies, those of SCPs were considerably lower (Fig 3C). The high average Pol II levels in the promoter-proximal regions may instead explain the low nucleosome occupancies, assuming competition between Pol II and nucleosome occupancy [46,53]. It is also possible that stable PIC scaffolds at the TATA boxes may compete with nucleosome formation. Thus, even though the low nucleosome occupancies of SCPs may appear to run contrary to the general association between high amplitudes and nucleosome occupancy (Fig 2C), their nucleosome forming potential is probably still high. It is notable that a high nucleosome forming potential and absence of permissive nucleosome features (H3K4me3, H2A.Z) were all associated with high circadian amplitudes. It appears that promoters with an active ground state are not generally employed to drive circadian transcription.
The establishment of a nucleosome-depleted chromatin state is apparently not widespread forDrosophilacircadian promoters, where no counterpart of mouse SCPs was found. This could mean that SCPs are a relatively late evolutionary invention, and that CCGs originally employed the type I promoter architecture thought to be common for heavily regulated genes.
The short nascent mRNA produced by Pol II prior to entering the paused state might act as a recruitment platform for regulators of transcription [95]. Thus, future investigations of circa-dian recruitment regulators of pause release at SCPs may also be directed to nascent mRNA interacting proteins. More broadly, investigators of clock-controlled genes may find the specific signatures of SCPs or type I circadian promoters useful to direct experimental designs when studying mechanisms of transcriptional regulation.
Methods
Promoters and CAGE data
Mouse promoters were compiled from the UCSC mm9 RefSeq annotation [96,97]. Then, CAGE clusters were computed based on the "FANTOM3and4"/"FANTOMtimecourseCAGE-mouse"/"liver_under_constant_darkness" data set as provided by the CAGEr/FANTO-M3and4CAGE R packages [57], using the recommended procedures in the package. CAGE peak widths were computed for locations ±300 bp of the TSSs provided by the UCSC RefSeq annotation, using the tagClusters function; the widths were defined as the distance between the 10% and 90% quantiles. The CAGE clusters represent mouse liver TSSs; for the cases that a CAGE cluster was measured within 300 bp of a RefSeq-annotated TSS, the CAGE location was used as a TSS instead, resulting in slight adjustments of 14585 out of 33333 RefSeq transcript TSSs. The distribution of CAGE peak widths was bimodal (S2D Fig), prompting a classification of promoters with a CAGE peak width smaller than 10 as focused, otherwise as dispersed.
Then, transcript sets with identical start and end coordinates were reduced to all but one transcript, leaving 27274 transcripts. Further, for TSSs corresponding to more than 1 resulting transcripts, only the shortest transcript was kept, 26251 transcripts now remained. Finally, a set of transcripts unambiguously assignable to a TSS was needed to conduct the present study, since it combines core promoter and transcript properties. For this, any overlapping transcripts with different TSSs needed to be discarded. Finally, transcripts shorter than 500 bp and non-protein coding transcripts (RefSeq annotation starting with "NR") were excluded, leaving a set of 17686 promoters and transcripts used for the following analysis.
Drosophilapromoters were compiled from the UCSC dm3 refGene table, which corre-sponds to the FlyBase transcript annotation [98]. The same procedure as for the mouse genome (above) resulted in 11096Drosophilapromoters.
Nascent-Seq, RNA-Seq, and microarray data
Mouse liver Nascent-Seq and RNA-Seq data from a study of mice kept under 12 hr/12 hr light-dark cycles [11] were obtained from the NCBI sequence read archive, accession numbers SRP011984 and SRP011981, respectively. Reads were aligned to the UCSC mm9 assembly using the Bowtie2/TopHat2 pipeline [99,100], allowing only uniquely mapped reads. Duplicate reads were removed. Transcripts were quantified against the USCS mm9 RefSeq annotation by counting reads mapping anywhere within the exons for Nascent-Seq, or reads compatible to splicing annotation for RNA-Seq; the R package GenomicAlignments [101] was used to design this workflow. Transcript abundances were reported as standard normalized RPKM values, with a further correction step using the calcNormFactors function of the R package edgeR [102].
Microarray data from samples of livers of mice kept under similar light-dark conditions [63] were obtained from NCBI GEO, accession number GSE33726, RMA normalized and sum-marized according to the RefSeq annotation using Brainarray v. 18 CDF files [103].
Drosophilahead Nascent-Seq and RNA-Seq data [12] were obtained from the NCBI sequence read archive, accession number SRP012175. Transcript abundances were quantified using the same workflow outlined above.
Circadian rhythms detection
Circadian rhythms in nascent or mature transcript abundances were detected using the RAIN algorithm and R package [104]. For both mouse liver andDrosophilaheads, data spanned two days, with samples taken every 4 hrs, yielding 12 samples for each Nascent-Seq or poly(A)+ RNA-Seq data set. Averages and relative amplitudes were quantified by harmonic regression using the HarmonicRegression R package [13]. Expressed transcripts were defined as tran-scripts having mean nascent RPKM values>0.1 (mouse) or>1 (Drosophila). Silent tran-scripts were defined as having mean nascent RPKM values of<0.01 in both organisms. Mouse circadian transcripts were defined as expressed transcripts having Benjamini-Hochberg corrected RAIN p values<0.2 and circadian amplitudes>0.1 as estimated by harmonic regression.Drosophilacircadian transcripts were defined as expressed transcripts having Ben-jamini-Hochberg corrected RAIN p values<0.25 (the laxer constraints onDrosophila tran-scripts were necessary in order to obtain a number of circadian trantran-scripts large enough for reliable statistics). Highly expressed transcripts were defined as the upper 25% quantile of mean transcriptional activities of expressed transcripts. High amplitude transcripts were defined as the upper 25% quantile of transcriptional amplitudes of circadian transcripts.
Detecting sequence features
Scans were made for TATA box PCM matches between−50 (start position) and−10 bp
(end position) of mouse andDrosophilaTSSs, and when plotting the positions of all mouse matches (not just the one closest to thea prioriconsensus position [108]), the familiar TATA box peak at around position−30 of the TSS [109] was recovered (S1A Fig), which validated the
present approach. The BREu and BREd PCMs were scanned for between the−75/−26 and the −30/−1 positions, respectively. Additionally filtering for evolutionary conservation improves
DrosophilaDNA binding site predictions, but this effect is less clear for mouse [110]. Hence, onlyDrosophilaTATA box hits with a median phastCons score>= 0.75 were retained. The PhastCons scores [111] were obtained from UCSC for theDrosophila melanogasterdm3 assembly, median phastCons scores were computed for each TATA box hit. For the promoters with more than one TATA box hit (these were often overlapping hits), the maximal median phastCons score was reported.
CpG ratios (observed/expected CG dinucleotides) were determined for mouse promoters using the standard formula CG×N/(G×C) [112], where N is the width of the DNA sequence considered. Here, an interval between−100 and +100 of the TSS was scanned for the CpG ratio
determination, so that N = 200 (by convention, there is no "0" position).
CTF ChIP-Seq data
Mouse liver ChIP-Seq peaks for BMAL1 (E-box binding) [4] were obtained from the Supple-mentary file doi:10.1371/journal.pbio.1000595.s019. Normalized tag counts for each time point as given in this file were used to estimate BMAL1 binding phases (ZT) using the HarmonicRe-gression package.
Mouse liver ChIP-Seq peaks for REV-ERBαandβ(ROR element binding) [5] were obtained from GEO (accession numbers GSM840528 and GSM840529, respectively).
Mouse liver ChIP-Seq peaks for E4BP4 (D-box binding) [6] were obtained from GEO (accession number GSM1437733). All mouse liver ChIP-Seq peaks were from reads aligned to the mouse mm9 assembly.
DrosophilaChIP-chip calls for CLK (E-box binding) [8] were obtained from the Supple-mentary Table 1 of that article, cycling peaks were retained. The chip used was based on the
Drosophiladm3 (BDGP5) assembly.
ChIP-Seq and ChIP-chip peaks were narrowed to their center coordinates, which were matched to regions ±3000 of the set of TSSs, except forDrosophilaCLK peaks, which were matched to ±2000 bp regions around the TSSs, following the original study. Promoter were classified as CTF binding if they had one or more CTF peak center within these regions.
Mouse liver Pol II and H3K4me3 ChIP-Seq data
ChIP-Seq reads were aligned to the mouse mm9 genome assembly using the BWA aligner [113]. Duplicate reads were removed, and aligned reads with a phred alignment quality of 30 or greater were retained.
Reads were mapped to the promoter regions using the GenomicAlignments package [101]; the "coverage" function was used to compute pileups for each promoter: reads per base per mil-lion reads (e.g.Fig 4A). Reads were the shifted equal amounts for top and bottom strands to maximize the correlation between coverages at both strands for regions spanning−500 to +500
